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Paper Citation Record · LEDGER

Watermarking LLMs with Weight Quantization

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.11237.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.11237 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:51:16.791786Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-17T22:16:04.880634Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 50578bbc-8f37-4b74-b09e-59f34cc1c03d · inbound

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities cites this paper.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities Watermarking LLMs with Weight Quantization

Reference 118

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:16:04.882685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T22:16:04.386706Z digest=sha256:fac0a3715283da66928b05e76f985fc1d0b219bcb76ff02fd3865398ccb1c778

Observation 2893c275-5934-45c0-a765-25bc9266d2ed · inbound

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models cites this paper.

MEraser: An Effective Fingerprint Erasure Approach for Large Language Models Watermarking LLMs with Weight Quantization

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T00:51:16.791786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:51:16.791786Z digest=sha256:12bff6f45596408fcb2e83be7568d29b125efffcaf4597e82ac9a0d6798e545c